All corporate networks are infected by malware. As the variability of malware samples has been rapidly increasing over the last years, existing signature-based security devices, firewalls, or anti-virus solutions provide only partial protection against these threats.
The ability to detect new variants and modifications of existing malware is becoming very important. Machine learning is beginning to be successfully applied to complement signature-based devices. However, machine learning methods require a large amount of labeled data for training, which limits their applicability due to high costs associated with labeling. Moreover, a malware detector is trained at a certain point in time, but malware evolves over time to evade detection.